The Client Playbook: Six Steps

If a client handed me their data estate tomorrow, this is the engagement: six steps, a weekly scorecard and a deliverable at every stage.

AI Projects › Client playbook

If a client handed me their data estate tomorrow, whether a marketing department’s media library, a manufacturer’s engineering shares or an agency’s twenty years of drives, this is the engagement. Every step has already been run at scale, on worse hardware.

Six steps

1. Week 1: rules of engagement and a metadata map

Agree the risk ladder and approval format before touching anything. Mount every source read-only. Walk metadata only: names, sizes, dates. Deliverable: a sized estate map and a schedule set by the slowest device.

2. Weeks 1 to 3: fingerprint everything, in parallel

One hash pass per disk, all disks at once, written to a database in real time. Each volume passes gates: counts match, every file is hashed, a random independent re-hash agrees. Measured rates were 30 to 100+ MiB/s per spinning disk. Deliverable: a live console the client can watch.

3. Week 3: the copy map and the risk register

Copies per content, single-copy exposure by owner and project, reclaim potential in TB and dollars, and broken or unplayable files. This build found 1,517 videos missing their index atom. Deliverable: a ranked work order, not a slide. See the copy map.

4. Week 4: protect, then reclaim

Copy single-copy data to a second home first, verified by hash. Pick golden records by health and completeness. Reclaim only where a hash survives on at least two other disks, with written approval for each batch. Deliverable: a trim manifest and a ledger of every removal.

5. Weeks 4 to 6: compress with lineage and gates

GPU encoding with triage rules, quality gates and a metadata parity gate. Every compressed child is linked to its parent hash. Deliverable: space returned, with proof of quality.

6. Then: make it searchable

Hybrid retrieval over the clean library: path, transcript and visual similarity, with every result keyed by fingerprint. Only after that, evaluate whether fine-tuning adds value. Deliverable: a library a person or an agent can search without losing track of identity.

Weekly scorecard

What I need from you

What is not claimed

Retrieval is designed, not in production. Fine-tuning is deferred. The agent message bus is designed, not built. The GPU lane is on hold. Client names are not published.

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